Keywords
machine learning algorithms, spatial predictions and mapping, software tool
Start Date
1-7-2008 12:00 AM
Abstract
Nowadays machine learning (ML), including Artificial Neural Networks (ANN) of different architectures and Support Vector Machines (SVM), provides extremely important tools for intelligent geo- and environmental data analysis, processing and visualisation. Machine learning is an important complement to the traditional techniques like geostatistics. This paper presents a review of several contemporary applications of ML for geospatial data: regional classification of environmental data, mapping of continuous environmental and pollution data, including the use of automatic algorithms, optimization (design/redesign) of monitoring networks.
Machine Learning Algorithms for GeoSpatial Data. Applications and Software Tools
Nowadays machine learning (ML), including Artificial Neural Networks (ANN) of different architectures and Support Vector Machines (SVM), provides extremely important tools for intelligent geo- and environmental data analysis, processing and visualisation. Machine learning is an important complement to the traditional techniques like geostatistics. This paper presents a review of several contemporary applications of ML for geospatial data: regional classification of environmental data, mapping of continuous environmental and pollution data, including the use of automatic algorithms, optimization (design/redesign) of monitoring networks.
